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POHow to Interpret Predict Result of SVM in R?
 

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  1. POHow to Interpret Predict Result of SVM in R?
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    <p>I'm new to R and I'm using the <code>e1071</code> package for SVM classification in R.</p> <p>I used the following code:</p> <pre><code>data &lt;- loadNumerical() model &lt;- svm(data[,-ncol(data)], data[,ncol(data)], gamma=10) print(predict(model, data[c(1:20),-ncol(data)])) </code></pre> <p>The <code>loadNumerical</code> is for loading data, and the data are of the form(first 8 columns are input and the last column is classification) :</p> <pre><code> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] 1 39 1 -1 43 -1 1 0 0.9050497 0 2 23 -1 -1 30 -1 -1 0 1.6624974 1 3 50 -1 -1 49 1 1 2 1.5571429 0 4 46 -1 1 19 -1 -1 0 1.3523685 0 5 36 1 1 29 -1 1 1 1.3812029 1 6 27 -1 -1 19 1 1 0 1.9403649 0 7 36 -1 -1 25 -1 1 0 2.3360004 0 8 41 1 1 23 1 -1 1 2.4899738 0 9 21 -1 -1 18 1 -1 2 1.2989637 1 10 39 -1 1 21 -1 -1 1 1.6121595 0 </code></pre> <p>The number of rows in the data is 500.</p> <p>As shown in the code above, I tested the first 20 rows for prediction. And the output is:</p> <pre><code> 1 2 3 4 5 6 7 0.04906014 0.88230392 0.04910760 0.04910719 0.87302217 0.04898187 0.04909523 8 9 10 11 12 13 14 0.04909199 0.87224979 0.04913189 0.04893709 0.87812890 0.04909588 0.04910999 15 16 17 18 19 20 0.89837037 0.04903778 0.04914173 0.04897789 0.87572114 0.87001066 </code></pre> <p>I can tell intuitively from the result that when the result is close to 0, it means 0 class, and if it's close to 1 it's in the 1 class.</p> <p>But my question is how can I <strong>precisely</strong> interpret the result: is there a threshold <em>s</em> I can use so that values below <em>s</em> are classified as 0 and values above <em>s</em> are classified as 1 ?</p> <p>If there exists such <em>s</em>, how can I derive it ?</p>
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    1. COIn future, make your examples reproducible. The line with loadNumeric isn't useful as it refers to a mystery function. A good strategy is to use dput on a small example and paste it to your question.
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    2. CO@IanFellows thanks for the tip.
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